Air Defense Threat Evaluation using Fuzzy Bayesian Classifier

Wei Mei

2013

Abstract

The connection between probability and fuzzy sets has been investigated among the community of approximate reasoning for decades. A typical viewpoint is that the grade of membership could be interpreted as a conditional probability. This note extend this viewpoint a step further by introducing the concepts of conditional probability mass function (CPMF) and the likelihood mass function (LMF). We draw the conclusion that conditional probability can be used for describing either randomness or fuzziness depending on how it is interpreted. If expanded to CPMF, then it can be used for modelling randomness; if expanded to LMF, then it can be a useful expression for modelling fuzziness. A fuzzy Bayesian theorem is derived based on the fuzziness interpretation of conditional probability. Its successful application to an example of target recognition demonstrates that the proposed fuzzy Bayesian theorem provides alternative approach for handling uncertainty.

Paper Citation

in Harvard Style

Mei W. (2013). Air Defense Threat Evaluation using Fuzzy Bayesian Classifier . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 227-232. DOI: 10.5220/0004512602270232

in Bibtex Style

@conference{fcta13,
author={Wei Mei},
title={Air Defense Threat Evaluation using Fuzzy Bayesian Classifier},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},
year={2013},
pages={227-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004512602270232},
isbn={978-989-8565-77-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - Air Defense Threat Evaluation using Fuzzy Bayesian Classifier
SN - 978-989-8565-77-8
AU - Mei W.
PY - 2013
SP - 227
EP - 232
DO - 10.5220/0004512602270232